Overview

Dataset statistics

Number of variables15
Number of observations48842
Missing cells0
Missing cells (%)0.0%
Duplicate rows52
Duplicate rows (%)0.1%
Total size in memory5.6 MiB
Average record size in memory120.0 B

Variable types

NUM13
BOOL2

Reproduction

Analysis started2020-08-25 01:03:14.635986
Analysis finished2020-08-25 01:03:45.792152
Duration31.16 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 52 (0.1%) duplicate rows Duplicates
workclass has 2799 (5.7%) zeros Zeros
education has 1389 (2.8%) zeros Zeros
marital-status has 6633 (13.6%) zeros Zeros
occupation has 2809 (5.8%) zeros Zeros
relationship has 19716 (40.4%) zeros Zeros
capital-gain has 44807 (91.7%) zeros Zeros
capital-loss has 46560 (95.3%) zeros Zeros
native-country has 857 (1.8%) zeros Zeros

Variables

age
Real number (ℝ≥0)

Distinct count74
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.64358543876172
Minimum17.0
Maximum90.0
Zeros0
Zeros (%)0.0%
Memory size381.7 KiB
2020-08-25T01:03:45.841510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile19
Q128
median37
Q348
95-th percentile63
Maximum90
Range73
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.71050993
Coefficient of variation (CV)0.35479394
Kurtosis-0.1842687406
Mean38.64358544
Median Absolute Deviation (MAD)10
Skewness0.5575803166
Sum1887430
Variance187.9780827
2020-08-25T01:03:45.945039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3613482.8%
 
3513372.7%
 
3313352.7%
 
2313292.7%
 
3113252.7%
 
3413032.7%
 
2812802.6%
 
3712802.6%
 
3012782.6%
 
3812642.6%
 
3212532.6%
 
4112352.5%
 
2712322.5%
 
2912232.5%
 
3912062.5%
 
2412062.5%
 
2511952.4%
 
4011872.4%
 
2211782.4%
 
4211652.4%
 
2611532.4%
 
2011132.3%
 
4311042.3%
 
4610972.2%
 
2110962.2%
 
Other values (49)1812037.1%
 
ValueCountFrequency (%) 
175951.2%
 
188621.8%
 
1910532.2%
 
2011132.3%
 
2110962.2%
 
2211782.4%
 
2313292.7%
 
2412062.5%
 
2511952.4%
 
2611532.4%
 
ValueCountFrequency (%) 
90550.1%
 
892< 0.1%
 
886< 0.1%
 
873< 0.1%
 
861< 0.1%
 
855< 0.1%
 
8413< 0.1%
 
8311< 0.1%
 
8215< 0.1%
 
81370.1%
 

workclass
Real number (ℝ≥0)

ZEROS

Distinct count9
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.870439375946931
Minimum0
Maximum8
Zeros2799
Zeros (%)5.7%
Memory size381.7 KiB
2020-08-25T01:03:46.057413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median4
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.46423368
Coefficient of variation (CV)0.3783120049
Kurtosis1.64197141
Mean3.870439376
Median Absolute Deviation (MAD)0
Skewness-0.7479097326
Sum189040
Variance2.14398027
2020-08-25T01:03:46.178417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
43390669.4%
 
638627.9%
 
231366.4%
 
027995.7%
 
719814.1%
 
516953.5%
 
114322.9%
 
821< 0.1%
 
310< 0.1%
 
ValueCountFrequency (%) 
027995.7%
 
114322.9%
 
231366.4%
 
310< 0.1%
 
43390669.4%
 
516953.5%
 
638627.9%
 
719814.1%
 
821< 0.1%
 
ValueCountFrequency (%) 
821< 0.1%
 
719814.1%
 
638627.9%
 
516953.5%
 
43390669.4%
 
310< 0.1%
 
231366.4%
 
114322.9%
 
027995.7%
 

fnlwgt
Real number (ℝ≥0)

Distinct count28523
Unique (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189664.13459727284
Minimum12285.0
Maximum1490400.0
Zeros0
Zeros (%)0.0%
Memory size381.7 KiB
2020-08-25T01:03:46.308809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12285
5-th percentile39615.4
Q1117550.5
median178144.5
Q3237642
95-th percentile379481.65
Maximum1490400
Range1478115
Interquartile range (IQR)120091.5

Descriptive statistics

Standard deviation105604.0254
Coefficient of variation (CV)0.5567949135
Kurtosis6.057848212
Mean189664.1346
Median Absolute Deviation (MAD)60295.5
Skewness1.438891879
Sum9263575662
Variance1.115221019e+10
2020-08-25T01:03:46.419680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20348821< 0.1%
 
19029019< 0.1%
 
12027719< 0.1%
 
12656918< 0.1%
 
12589218< 0.1%
 
12667517< 0.1%
 
9918517< 0.1%
 
11336417< 0.1%
 
18693416< 0.1%
 
11156716< 0.1%
 
12765115< 0.1%
 
12013115< 0.1%
 
12301115< 0.1%
 
11796315< 0.1%
 
12398314< 0.1%
 
14899514< 0.1%
 
12112414< 0.1%
 
10814014< 0.1%
 
19388214< 0.1%
 
18824614< 0.1%
 
11148314< 0.1%
 
19463014< 0.1%
 
13287914< 0.1%
 
16419014< 0.1%
 
13698614< 0.1%
 
Other values (28498)4845099.2%
 
ValueCountFrequency (%) 
122851< 0.1%
 
134921< 0.1%
 
137693< 0.1%
 
138621< 0.1%
 
148781< 0.1%
 
188271< 0.1%
 
192141< 0.1%
 
193026< 0.1%
 
193952< 0.1%
 
194102< 0.1%
 
ValueCountFrequency (%) 
14904001< 0.1%
 
14847051< 0.1%
 
14554351< 0.1%
 
13661201< 0.1%
 
12683391< 0.1%
 
12265831< 0.1%
 
12105041< 0.1%
 
11846221< 0.1%
 
11613631< 0.1%
 
11256131< 0.1%
 

education
Real number (ℝ≥0)

ZEROS

Distinct count16
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.288419802628885
Minimum0
Maximum15
Zeros1389
Zeros (%)2.8%
Memory size381.7 KiB
2020-08-25T01:03:46.530412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median11
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.874492433
Coefficient of variation (CV)0.3765877081
Kurtosis0.6765763119
Mean10.2884198
Median Absolute Deviation (MAD)2
Skewness-0.9362986762
Sum502507
Variance15.01169162
2020-08-25T01:03:46.644536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
111578432.3%
 
151087822.3%
 
9802516.4%
 
1226575.4%
 
820614.2%
 
118123.7%
 
716013.3%
 
013892.8%
 
59552.0%
 
148341.7%
 
67561.5%
 
26571.3%
 
105941.2%
 
45091.0%
 
32470.5%
 
13830.2%
 
ValueCountFrequency (%) 
013892.8%
 
118123.7%
 
26571.3%
 
32470.5%
 
45091.0%
 
59552.0%
 
67561.5%
 
716013.3%
 
820614.2%
 
9802516.4%
 
ValueCountFrequency (%) 
151087822.3%
 
148341.7%
 
13830.2%
 
1226575.4%
 
111578432.3%
 
105941.2%
 
9802516.4%
 
820614.2%
 
716013.3%
 
67561.5%
 

education-num
Real number (ℝ≥0)

Distinct count16
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.078088530363212
Minimum1.0
Maximum16.0
Zeros0
Zeros (%)0.0%
Memory size381.7 KiB
2020-08-25T01:03:46.765154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q312
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.570972756
Coefficient of variation (CV)0.2551051966
Kurtosis0.6257452728
Mean10.07808853
Median Absolute Deviation (MAD)1
Skewness-0.3165248567
Sum492234
Variance6.60990091
2020-08-25T01:03:46.873644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
91578432.3%
 
101087822.3%
 
13802516.4%
 
1426575.4%
 
1120614.2%
 
718123.7%
 
1216013.3%
 
613892.8%
 
49552.0%
 
158341.7%
 
57561.5%
 
86571.3%
 
165941.2%
 
35091.0%
 
22470.5%
 
1830.2%
 
ValueCountFrequency (%) 
1830.2%
 
22470.5%
 
35091.0%
 
49552.0%
 
57561.5%
 
613892.8%
 
718123.7%
 
86571.3%
 
91578432.3%
 
101087822.3%
 
ValueCountFrequency (%) 
165941.2%
 
158341.7%
 
1426575.4%
 
13802516.4%
 
1216013.3%
 
1120614.2%
 
101087822.3%
 
91578432.3%
 
86571.3%
 
718123.7%
 

marital-status
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6187502559272757
Minimum0
Maximum6
Zeros6633
Zeros (%)13.6%
Memory size381.7 KiB
2020-08-25T01:03:46.995538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.507702551
Coefficient of variation (CV)0.5757336146
Kurtosis-0.5361939917
Mean2.618750256
Median Absolute Deviation (MAD)2
Skewness-0.01632824007
Sum127905
Variance2.273166981
2020-08-25T01:03:47.098825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
22237945.8%
 
41611733.0%
 
0663313.6%
 
515303.1%
 
615183.1%
 
36281.3%
 
1370.1%
 
ValueCountFrequency (%) 
0663313.6%
 
1370.1%
 
22237945.8%
 
36281.3%
 
41611733.0%
 
515303.1%
 
615183.1%
 
ValueCountFrequency (%) 
615183.1%
 
515303.1%
 
41611733.0%
 
36281.3%
 
22237945.8%
 
1370.1%
 
0663313.6%
 

occupation
Real number (ℝ≥0)

ZEROS

Distinct count15
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.57769952090414
Minimum0
Maximum14
Zeros2809
Zeros (%)5.8%
Memory size381.7 KiB
2020-08-25T01:03:47.204739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.230509418
Coefficient of variation (CV)0.6431594213
Kurtosis-1.236280489
Mean6.577699521
Median Absolute Deviation (MAD)4
Skewness0.1105506005
Sum321268
Variance17.89720993
2020-08-25T01:03:47.309810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10617212.6%
 
3611212.5%
 
4608612.5%
 
1561111.5%
 
12550411.3%
 
8492310.1%
 
730226.2%
 
028095.8%
 
1423554.8%
 
620724.2%
 
514903.1%
 
1314463.0%
 
119832.0%
 
92420.5%
 
215< 0.1%
 
ValueCountFrequency (%) 
028095.8%
 
1561111.5%
 
215< 0.1%
 
3611212.5%
 
4608612.5%
 
514903.1%
 
620724.2%
 
730226.2%
 
8492310.1%
 
92420.5%
 
ValueCountFrequency (%) 
1423554.8%
 
1314463.0%
 
12550411.3%
 
119832.0%
 
10617212.6%
 
92420.5%
 
8492310.1%
 
730226.2%
 
620724.2%
 
514903.1%
 

relationship
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4432865157036976
Minimum0
Maximum5
Zeros19716
Zeros (%)40.4%
Memory size381.7 KiB
2020-08-25T01:03:47.423923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.602151226
Coefficient of variation (CV)1.110071499
Kurtosis-0.7541163751
Mean1.443286516
Median Absolute Deviation (MAD)1
Skewness0.7917193051
Sum70493
Variance2.56688855
2020-08-25T01:03:47.526137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01971640.4%
 
11258325.8%
 
3758115.5%
 
4512510.5%
 
523314.8%
 
215063.1%
 
ValueCountFrequency (%) 
01971640.4%
 
11258325.8%
 
215063.1%
 
3758115.5%
 
4512510.5%
 
523314.8%
 
ValueCountFrequency (%) 
523314.8%
 
4512510.5%
 
3758115.5%
 
215063.1%
 
11258325.8%
 
01971640.4%
 

race
Real number (ℝ≥0)

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6680520863191517
Minimum0
Maximum4
Zeros470
Zeros (%)1.0%
Memory size381.7 KiB
2020-08-25T01:03:47.638414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median4
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8459860031
Coefficient of variation (CV)0.2306363114
Kurtosis4.951859866
Mean3.668052086
Median Absolute Deviation (MAD)0
Skewness-2.447812798
Sum179155
Variance0.7156923174
2020-08-25T01:03:47.748263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
44176285.5%
 
246859.6%
 
115193.1%
 
04701.0%
 
34060.8%
 
ValueCountFrequency (%) 
04701.0%
 
115193.1%
 
246859.6%
 
34060.8%
 
44176285.5%
 
ValueCountFrequency (%) 
44176285.5%
 
34060.8%
 
246859.6%
 
115193.1%
 
04701.0%
 

sex
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
1
32650
0
16192
ValueCountFrequency (%) 
13265066.8%
 
01619233.2%
 

capital-gain
Real number (ℝ≥0)

ZEROS

Distinct count123
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1079.0676262233324
Minimum0.0
Maximum99999.0
Zeros44807
Zeros (%)91.7%
Memory size381.7 KiB
2020-08-25T01:03:47.874397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5013
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7452.019058
Coefficient of variation (CV)6.905979641
Kurtosis152.6930963
Mean1079.067626
Median Absolute Deviation (MAD)0
Skewness11.894659
Sum52703821
Variance55532588.04
2020-08-25T01:03:48.127479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
04480791.7%
 
150245131.1%
 
76884100.8%
 
72983640.7%
 
999992440.5%
 
31031520.3%
 
51781460.3%
 
50131170.2%
 
43861080.2%
 
8614820.2%
 
3325810.2%
 
2174740.2%
 
10520640.1%
 
4650630.1%
 
27828580.1%
 
4064540.1%
 
594520.1%
 
3137510.1%
 
20051490.1%
 
14084490.1%
 
13550420.1%
 
6849420.1%
 
3908420.1%
 
2829420.1%
 
1055370.1%
 
Other values (98)10992.3%
 
ValueCountFrequency (%) 
04480791.7%
 
1148< 0.1%
 
4015< 0.1%
 
594520.1%
 
91410< 0.1%
 
9916< 0.1%
 
1055370.1%
 
10868< 0.1%
 
11111< 0.1%
 
115113< 0.1%
 
ValueCountFrequency (%) 
999992440.5%
 
413103< 0.1%
 
340956< 0.1%
 
27828580.1%
 
2523614< 0.1%
 
251246< 0.1%
 
220401< 0.1%
 
20051490.1%
 
184812< 0.1%
 
158318< 0.1%
 

capital-loss
Real number (ℝ≥0)

ZEROS

Distinct count99
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.50231358257237
Minimum0.0
Maximum4356.0
Zeros46560
Zeros (%)95.3%
Memory size381.7 KiB
2020-08-25T01:03:48.234338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4356
Range4356
Interquartile range (IQR)0

Descriptive statistics

Standard deviation403.0045521
Coefficient of variation (CV)4.605644532
Kurtosis20.01434595
Mean87.50231358
Median Absolute Deviation (MAD)0
Skewness4.569808858
Sum4273788
Variance162412.669
2020-08-25T01:03:48.325672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
04656095.3%
 
19023040.6%
 
19772530.5%
 
18872330.5%
 
2415720.1%
 
1485710.1%
 
1848670.1%
 
1602620.1%
 
1590620.1%
 
1876590.1%
 
1740580.1%
 
1672500.1%
 
1741440.1%
 
1564430.1%
 
2258390.1%
 
1719380.1%
 
1980360.1%
 
1408350.1%
 
2001350.1%
 
1669350.1%
 
2002330.1%
 
1579300.1%
 
2051290.1%
 
1974280.1%
 
1721280.1%
 
Other values (74)5381.1%
 
ValueCountFrequency (%) 
04656095.3%
 
1551< 0.1%
 
2135< 0.1%
 
3235< 0.1%
 
4193< 0.1%
 
62517< 0.1%
 
6534< 0.1%
 
8102< 0.1%
 
8806< 0.1%
 
9742< 0.1%
 
ValueCountFrequency (%) 
43563< 0.1%
 
39002< 0.1%
 
37704< 0.1%
 
36832< 0.1%
 
31752< 0.1%
 
30045< 0.1%
 
282414< 0.1%
 
27542< 0.1%
 
26037< 0.1%
 
255917< 0.1%
 

hours-per-week
Real number (ℝ≥0)

Distinct count96
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.422382375824085
Minimum1.0
Maximum99.0
Zeros0
Zeros (%)0.0%
Memory size381.7 KiB
2020-08-25T01:03:48.426936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.05
Q140
median40
Q345
95-th percentile60
Maximum99
Range98
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.39144402
Coefficient of variation (CV)0.3065490774
Kurtosis2.95105909
Mean40.42238238
Median Absolute Deviation (MAD)3
Skewness0.2387496572
Sum1974310
Variance153.547885
2020-08-25T01:03:48.521372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
402280346.7%
 
5042468.7%
 
4527175.6%
 
6021774.5%
 
3519374.0%
 
2018623.8%
 
3017003.5%
 
5510512.2%
 
259582.0%
 
487701.6%
 
387141.5%
 
156231.3%
 
704370.9%
 
104250.9%
 
324230.9%
 
653550.7%
 
243540.7%
 
423380.7%
 
363360.7%
 
443100.6%
 
163030.6%
 
122470.5%
 
372420.5%
 
432270.5%
 
82180.4%
 
Other values (71)30696.3%
 
ValueCountFrequency (%) 
1270.1%
 
2530.1%
 
3590.1%
 
4840.2%
 
5950.2%
 
6920.2%
 
7450.1%
 
82180.4%
 
9270.1%
 
104250.9%
 
ValueCountFrequency (%) 
991370.3%
 
9814< 0.1%
 
972< 0.1%
 
969< 0.1%
 
952< 0.1%
 
941< 0.1%
 
923< 0.1%
 
913< 0.1%
 
90420.1%
 
893< 0.1%
 

native-country
Real number (ℝ≥0)

ZEROS

Distinct count42
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.74935506326522
Minimum0
Maximum41
Zeros857
Zeros (%)1.8%
Memory size381.7 KiB
2020-08-25T01:03:48.627730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q139
median39
Q339
95-th percentile39
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.775343161
Coefficient of variation (CV)0.2115776766
Kurtosis12.77229318
Mean36.74935506
Median Absolute Deviation (MAD)0
Skewness-3.689528549
Sum1794912
Variance60.45596128
2020-08-25T01:03:48.721284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
394383289.7%
 
269511.9%
 
08571.8%
 
302950.6%
 
112060.4%
 
331840.4%
 
21820.4%
 
81550.3%
 
191510.3%
 
51380.3%
 
91270.3%
 
31220.2%
 
351150.2%
 
231060.2%
 
221050.2%
 
61030.2%
 
24920.2%
 
13880.2%
 
31870.2%
 
40860.2%
 
4850.2%
 
14750.2%
 
32670.1%
 
36650.1%
 
20590.1%
 
Other values (17)5091.0%
 
ValueCountFrequency (%) 
08571.8%
 
1280.1%
 
21820.4%
 
31220.2%
 
4850.2%
 
51380.3%
 
61030.2%
 
7450.1%
 
81550.3%
 
91270.3%
 
ValueCountFrequency (%) 
4123< 0.1%
 
40860.2%
 
394383289.7%
 
38270.1%
 
37300.1%
 
36650.1%
 
351150.2%
 
3421< 0.1%
 
331840.4%
 
32670.1%
 

target
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
1
37155
0
11687
ValueCountFrequency (%) 
13715576.1%
 
01168723.9%
 

Interactions

2020-08-25T01:03:16.943178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:17.109931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:17.281119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:17.456569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:17.622641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:17.788684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:17.959049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:18.127465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:18.285095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:18.452883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:18.613184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:18.769826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:18.932310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:19.086091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:19.257435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:19.432335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:19.609056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:19.779388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:20.126382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:20.298545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:20.472601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:20.636770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:20.813356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:20.989752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:21.150842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:21.318150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:21.477705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:21.645777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:21.822051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:21.997539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:22.164096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:22.331540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:22.501799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:22.665627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:22.832622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.011625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.182976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.346884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.503664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.657681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.823228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:23.988672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:24.150120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:24.312146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:24.474906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:24.643862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:24.808238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:25.153961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:25.320828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:25.478911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:25.631839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:25.786865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:25.935471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:26.101466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:26.263666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:26.424622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:26.587328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:26.756881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:26.920230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:27.105975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:27.263265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:27.430858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:27.588622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:27.739922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:27.907263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:28.058037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:28.216190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:28.380886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:28.541175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:28.700913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:28.861589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:29.013030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:29.167761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:29.318402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:29.477163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:29.639187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:29.947034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:30.098300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:30.255535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:30.423345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:30.590389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:30.754662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:30.928719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:31.087100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:31.243631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:31.405998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:31.562735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:31.725908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:31.883123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.034470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.189537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.339794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.495912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.648902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.804331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:32.963766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:33.120547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:33.271445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:33.428325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:33.571989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:33.730953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:33.891052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:34.037676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:34.185183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:34.328571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:34.655971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:34.825769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:35.007544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:35.177006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:35.347535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:35.525873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:35.697362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:35.859633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:36.037549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:36.201032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:36.366352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:36.535307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:36.694774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:36.847860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.013994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.182972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.343915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.510435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.665079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.822457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:37.975920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:38.136875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:38.287644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:38.433232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:38.593731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:38.747244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:38.903763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:39.060101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:39.215932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:39.591113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:39.753992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:39.919787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.079772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.223044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.376709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.520847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.664760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.814901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:40.958619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:41.119368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:41.280808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:41.442222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:41.604641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:41.769606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:41.926391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:42.088341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:42.242275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:42.406448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:42.561471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:42.712353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:42.865148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.008867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.154946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.299942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.451289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.598192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.744519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:43.898352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:44.043242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:44.184555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:44.505240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:44.648286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:44.787468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:44.924226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:03:48.845202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:03:49.111509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:03:49.389241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:03:49.663739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:03:45.192176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:03:45.566275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget
039.0777516.0913.0411412174.00.040.0391
150.0683311.0913.0240410.00.013.0391
238.04215646.0119.0061410.00.040.0391
353.04234721.017.0260210.00.040.0391
428.04338409.0913.02105200.00.040.051
537.04284582.01214.0245400.00.040.0391
649.04160187.065.0381200.00.016.0231
752.06209642.0119.0240410.00.045.0390
831.0445781.01214.041014014084.00.050.0390
942.04159449.0913.0240415178.00.040.0390

Last rows

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget
4883261.0489686.0119.02120410.00.048.0391
4883331.04440129.0119.0230410.00.040.0391
4883425.04350977.0119.0483400.00.040.0391
4883548.02349230.01214.0081410.00.040.0391
4883633.04245211.0913.04103410.00.040.0391
4883739.04215419.0913.00101400.00.036.0391
4883864.00321403.0119.0602210.00.040.0391
4883938.04374983.0913.02100410.00.050.0391
4884044.0483891.0913.0013115455.00.040.0391
4884135.05182148.0913.0240410.00.060.0390

Duplicate rows

Most frequent

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytargetcount
1221.04243368.0131.0451410.00.050.02613
2325.04195994.032.0491400.00.040.01313
2425.04308144.0913.0431410.00.040.02613
017.04153021.028.04123400.00.020.03912
118.05378036.028.0453410.00.010.03912
219.00167428.01510.0403410.00.040.03912
319.0497261.0119.0451410.00.040.03912
419.04130431.043.0451410.00.036.02612
519.04138153.01510.0413400.00.010.03912
619.04139466.01510.04123400.00.025.03912